Ce-Sn-W-O_x催化剂的配伍优化及脱硝影响因素

采用共混热解法制备系列Ce-Sn-W-Ox复合氧化物,用于NH3选择性催化还原NO。通过正交实验优化CeSn-W-Ox配方,采用环境扫描电镜(ESEM)、X-射线衍射仪(XRD)等表征分析催化剂的微观形貌和固相结构,确立Ce-Sn-WOx最佳配比及结构形貌。结果表明,以粒度为5~8 mm的堇青石瓷片担载分散Ce-Sn-W-Ox,进行NH3-SCR脱除NO,当Ce/Sn/W元素摩尔比为1∶0.8∶0.6时,Ce Sn0.8W0.6Ox/堇青石NH3-SCR脱除NO效果最好。当空速为7 200 h-1,催化剂在252~426℃内脱除NO效率均大于94%。重点考察了反应空速(GHSV)、水蒸气(H2O)、SO2等对Ce Sn0.8W0.6Ox/堇青石NH3-SCR脱除NO活性的影响。研究表明,空速低于10 000 h-1时,催化剂脱硝活性受空速影响小;单独通入5%H2O对催化剂脱硝活性基本没有影响;单独通入429 mg/m3SO2导致催化剂活性略有降低;同时通入429 mg/m3SO2和5%H2O,催化剂脱硝活性下降至85.33%,除去SO2和H2O后,催化剂活性又能明显回升。     

The need for spatially explicit quantification of benefits in invasive‐species management

Worldwide, invasive species are a leading driver of environmental change across terrestrial, marine, and freshwater environments and cost billions of dollars annually in ecological damages and economic losses. Resources limit invasive‐species control, and planning processes are needed to identify cost‐effective solutions. Thus, studies are increasingly considering spatially variable natural and socioeconomic assets (e.g., species persistence, recreational fishing) when planning the allocation of actions for invasive‐species management. There is a need to improve understanding of how such assets are considered in invasive‐species management. We reviewed over 1600 studies focused on management of invasive species, including flora and fauna. Eighty‐four of these studies were included in our final analysis because they focused on the prioritization of actions for invasive species management. Forty‐five percent (n = 38) of these studies were based on spatial optimization methods, and 35% (n = 13) accounted for spatially variable assets. Across all 84 optimization studies considered, 27% (n = 23) explicitly accounted for spatially variable assets. Based on our findings, we further explored the potential costs and benefits to invasive species management when spatially variable assets are explicitly considered or not. To include spatially variable assets in decision‐making processes that guide invasive‐species management there is a need to quantify environmental responses to invasive species and to enhance understanding of potential impacts of invasive species on different natural or socioeconomic assets. We suggest these gaps could be filled by systematic reviews, quantifying invasive species impacts on native species at different periods, and broadening sources and enhancing sharing of knowledge.     

A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis

IntroductionWith the development of industries and increased diversity of their associated hazards, the importance of identifying these hazards and controlling the Occupational Health and Safety (OHS) risks has also dramatically augmented. Currently, there is a serious need for a risk management system to identify and prioritize risks with the aim of providing corrective/preventive measures to minimize the negative consequences of OHS risks. In fact, this system can help the protection of employees’ health and reduction of organizational costs. Method: The present study proposes a hybrid decision-making approach based on the Failure Mode and Effect Analysis (FMEA), Fuzzy Cognitive Map (FCM), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for assessing and prioritizing OHS risks. After identifying the risks and determining the values of the risk assessment criteria via the FMEA technique, the attempt is made to determine the weights of criteria based on their causal relationships through FCM and the hybrid learning algorithm. Then, the risk prioritization is carried out using the MOORA method based on the decision matrix (the output of the FMEA) and the weights of the criteria (the output of the FCM). Results: The results from the implementation of the proposed approach in a manufacturing company reveal that the score at issue can overcome some of the drawbacks of the traditional Risk Priority Number (RPN) in the conventional FMEA, including lack of assignment the different relative importance to the assessment criteria, inability to take into account other important management criteria, lack of consideration of causal relationships among criteria, and high dependence of the prioritization on the experts’ opinions, which finally provides a full and distinct risk prioritization.     

Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.     

Comparative evaluation of sizing of metal hydride (MH) hydrogen storage tank filled with different alloys

This study presents a comparative analysis of sizing of metal hydride tank filled with different alloys. Alloys include solid solutions and intermetallic compounds of the generic families AB₅, AB₂, AB, A₂B. The effects of the different alloys on the sizing of metal hydride hydrogen storage tanks are complicated and depend on many factors. In this paper, a thermoeconomic optimization analysis with a simple algebraic formula was presented for the estimation of optimum metal hydride tank surface area for heat transfer enhancement. The optimum area of the metal hydride tank filled with commercially available different alloys (LaN₅, Ti_0,98Zr_0,02V_0,43Fe_0,09Cr_0,05Mn_1,5, TiFe, Mg₂NiH₄) was evaluated and compared by the developed method. The optimum net savings and the value of payback were determined for four alloys. It is found that mathematical model can be employed for the determination of optimum metal hydride tank design and increasing net savings according to alloy types. The optimum areas of the tanks filled with four alloys (LaN₅, Ti_0,98Zr_0,02V_0,43Fe_0,09Cr_0,05Mn_1,5, TiFe, Mg₂NiH₄) were calculated as 0.136, 0.130, 0.133, and 0.173 m², respectively. The optimum net savings for tanks filled with four alloys (LaN₅, Ti_0,98Zr_0,02V_0,43Fe_0,09Cr_0,05Mn_1,5, TiFe, Mg₂NiH₄) are about 461.0, 409.3, 419.6, and 979.6 $ and the values of payback are about 1.98, 2.1, 2.17, and 1.37 years, respectively. Excessive area of the metal hydride tank would not be as economical as the optimum tank area. Thermal management of metal hydride tank must be designed for optimum points calculated at which maximum savings occur.     

湖北省土地利用减碳增效系统仿真及结构优化研究

土地利用变化是引起碳排放的重要原因之一,土地经济效益是利用土地的目标之一,如何通过优化土地利用结构实现土地利用减碳增效是值得研究的重要问题。基于系统结构与功能相互作用的视角,梳理复杂系统内变量间的反馈关系,运用系统动力学(SD)进行建模,将约束条件纳入到多目标规划(MOP)中,实现MOP与SD模型整合,进行系统仿真并得出优化后2020年湖北省土地利用结构。结果显示,利用SD-MOP模型能够实现减碳增效目标下土地利用结构优化,与2008年真实值相比,耕地、林地、牧草地及建设用地分别增加了0.33×10~4、30.17×10~4、0.08×10~4和16.37×10~4 hm~2,其他农用地及未利用地分别减少7.23×10~4、33.15×10~4 hm~2;与无约束SD单模型仿真相比,土地利用碳排放量减少了58×10~4 t,经济效益年增长率维持在3.58%,优化方案具有可行性。SD-MOP模型优化的土地利用结构符合区域可持续发展要求,兼顾了土地利用碳减排和经济效益增长的双重目标,能为区域土地资源优化配置提供参考。     

Optimization-Based Atmospheric Plume Source Identification

This work applies optimization and an Eulerian inversion approach presented by Bagtzoglou and Baun in 2005 in order to reconstruct contaminant plume time histories and to identify the likely source of atmospheric contamination using data from a real test site for the first time. Present-day distribution of an atmospheric contaminant plume as well as data points reflecting the plume history allow the reconstruction and provide the plume velocity, distribution, and probable source. The method was tested to a hypothetical case and with data from the Forest Atmosphere Transfer and Storage (FACTS) experiment in the Duke experimental forest site. In the scenarios presented herein, as well as in numerous cases tested for verification purposes, the model conserved mass, successfully located the peak of the plume, and managed to capture the motion of the plume well but underestimated the contaminant peak.     

基于改进粒子群算法和元胞自动机的城市扩张模拟——以南京为例

为合理利用多智能体算法解决城市扩张动态模拟问题,基于地理学理论和社会学规律对粒子群算法进行有针对性的改进,提出分段式粒子群算法(SPSO),并结合元胞自动机模拟复杂时空过程的能力,构建出适用于城市扩张模拟的地理元胞自动机SPSO-CA。在SPSO-CA中我们利用多时像的土地利用数据、交通路网数据和地形数据,挖掘出1995~2000年南京城市扩张的土地转换规则。再由此规则实现1995~2008年的南京市城市扩张过程的动态模拟。最后对比SPSO-CA、PSOCA及NULL模型结果得:SPSO-CA总精度86.3%,Kappa系数为0.792,Moran’s I为0.078,PSO-CA总精度83.6%,Kappa系数为0.755,Moran’s I为0.054,NULL模型总精度81.9%,Kappa系数为0.741,真实的Moran’s I为0.072。这表明无论是总精度还是空间一致性,SPSO-CA都优于PSO-CA和NULL模型,即用SPSO-CA模拟城市扩张是可行的。     

炼油废水污泥脱水工艺条件的优化

采用化学除油降黏—污泥调理—离心脱水工艺处理某炼油厂废水处理系统的混合污泥，并对工艺条件进行优化。实验结果表明，最佳的工艺条件为：化学除油降黏阶段处理体系的pH=4，反应温度35 ℃，H₂O₂加入量 2 g/L，m（H₂O₂）∶ m（Fe²⁺）=4，反应时间 60 min；污泥调理反应阶段的CaO加入量7.0 g/L；离心脱水阶段在分离因数为1 558时脱水5 min。在此条件下，得到的泥饼的含水率为70.0%~75.0%（w），含油率小于2%（w），污泥比阻约为3.0×10⁷ s²/g。     

Fine‐resolution conservation planning with limited climate‐change information

Climate‐change induced uncertainties in future spatial patterns of conservation‐related outcomes make it difficult to implement standard conservation‐planning paradigms. A recent study translates Markowitz's risk‐diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate‐change scenarios for carrying out fine‐resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk‐return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate‐change information and full climate‐change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate‐change forecasts such that the best possible risk‐return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate‐change information could be reduced by 17% relative to other iterative approaches.